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Register a new userEnergy filtering with X-ray lenses: optimization for photon-counting mammography
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Chromatic properties of the multi-prism and prism-array X-ray lenses (MPL and PAL) can potentially be utilized for efficient energy filtering and dose reduction in mammography. The line-shaped foci of the lenses are optimal for coupling to photon-counting silicon strip detectors in a scanning system. A theoretical model was developed and used to investigate the benefit of two lenses compared with an absorption-filtered reference system. The dose reduction of the MPL filter was ~15% compared with the reference system at matching scan time, and the spatial resolution was higher. The dose of the PAL-filtered system was found to be ~20% lower than for the reference system at equal scan time and resolution, and only ~20% higher than for a monochromatic beam. An investigation of some practical issues remains, including the feasibility of brilliant-enough X-ray sources and manufacturing of a polymer PAL.
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Conventional energy filters for x-ray imaging are based on absorbing materials which attenuate low energy photons, sometimes combined with an absorption edge, thus also discriminating towards photons of higher energies. These filters are fairly inefficient, in particular for photons of higher energies, and other methods for achieving a narrower bandwidth have been proposed. Such methods include various types of monochromators, based on for instance mosaic crystals or refractive multi-prism x-ray lenses (MPLs). Prism-array lenses (PALs) are similar to MPLs, but are shorter, have larger apertures, and higher transmission. A PAL consists of a number of small prisms arranged in columns perpendicular to the optical axis. The column height decreases along the optical axis so that the projection of lens material is approximately linear with a Fresnel phase-plate pattern superimposed on it. The focusing effect is one dimensional, and the lens is chromatic. Hence, unwanted energies can be blocked by placing a slit in the image plane of a desired energy. We present the first experimental and theoretical results on an energy filter based on a silicon PAL. The study includes an evaluation of the spectral shaping properties of the filter as well as a quantification of the achievable increase in dose efficiency compared to standard methods. Previously, PALs have been investigated with synchrotron radiation, but in this study a medical imaging setup, based on a regular x-ray tube, is considered.
Phase-contrast imaging is an emerging technology that may increase the signal-difference-to-noise ratio in medical imaging. One of the most promising phase-contrast techniques is Talbot interferometry, which, combined with energy-sensitive photon-counting detectors, enables spectral differential phase-contrast mammography. We have evaluated a realistic system based on this technique by cascaded-systems analysis and with a task-dependent ideal-observer detectability index as a figure-of-merit. Beam-propagation simulations were used for validation and illustration of the analytical framework. Differential phase contrast improved detectability compared to absorption contrast, in particular for fine tumor structures. This result was supported by images of human mastectomy samples that were acquired with a conventional detector. The optimal incident energy was higher in differential phase contrast than in absorption contrast when disregarding the setup design energy. Further, optimal weighting of the transmitted spectrum was found to have a weaker energy dependence than for absorption contrast. Taking the design energy into account yielded a superimposed maximum on both detectability as a function of incident energy, and on optimal weighting. Spectral material decomposition was not facilitated by phase contrast, but phase information may be used instead of spectral information.
Beam quality optimization in mammography traditionally considers detection of a target obscured by quantum noise on a homogenous background. It can be argued that this scheme does not correspond well to the clinical imaging task because real mammographic images contain a complex superposition of anatomical structures, resulting in anatomical noise that may dominate over quantum noise. Using a newly developed spectral mammography system, we measured the correlation and magnitude of the anatomical noise in a set of mammograms. The results from these measurements were used as input to an observer-model optimization that included quantum noise as well as anatomical noise. We found that, within this framework, the detectability of tumors and microcalcifications behaved very differently with respect to beam quality and dose. The results for small microcalcifications were similar to what traditional optimization methods would yield, which is to be expected since quantum noise dominates over anatomical noise at high spatial frequencies. For larger tumors, however, low-frequency anatomical noise was the limiting factor. Because anatomical structure has similar energy dependence as tumor contrast, optimal x-ray energy was significantly higher and the useful energy region wider than traditional methods suggest. Measurements on a tissue phantom confirmed these theoretical results. Furthermore, since quantum noise constitutes only a small fraction of the noise, the dose could be reduced substantially without sacrificing tumor detectability. Exposure settings used clinically are therefore not necessarily optimal for this imaging task. The impact of these findings on the mammographic imaging task as a whole is, however, at this stage unclear.
We present the first evaluation of a recently developed silicon-strip detector for photon-counting dual-energy breast tomosynthesis. The detector is well suited for tomosynthesis with high dose efficiency and intrinsic scatter rejection. A method was developed for measuring the spatial resolution of a system based on the detector in terms of the three-dimensional modulation transfer function (MTF). The measurements agreed well with theoretical expectations, and it was seen that depth resolution was won at the cost of a slightly decreased lateral resolution. This may be a justifiable trade-off as clinical images acquired with the system indicate improved conspicuity of breast lesions. The photon-counting detector enables dual-energy subtraction imaging with electronic spectrumsplitting. This improved the detectability of iodine in phantom measurements, and the detector was found to be stable over typical clinical acquisition times. A model of the energy resolution showed that further improvements are within reach by optimization of the detector.
Spectral imaging is a method in medical x-ray imaging to extract information about the object constituents by the material-specific energy dependence of x-ray attenuation. Contrast-enhanced spectral imaging has been thoroughly investigated, but unenhanced imaging may be more useful because it comes as a bonus to the conventional non-energy-resolved absorption image at screening; there is no additional radiation dose and no need for contrast medium. We have used a previously developed theoretical framework and system model that include quantum and anatomical noise to characterize the performance of a photon-counting spectral mammography system with two energy bins for unenhanced imaging. The theoretical framework was validated with synthesized images. Optimal combination of the energy-resolved images for detecting large unenhanced tumors corresponded closely, but not exactly, to minimization of the anatomical noise, which is commonly referred to as energy subtraction. In that case, an ideal-observer detectability index could be improved close to 50% compared to absorption imaging. Optimization with respect to the signal-to-quantum-noise ratio, commonly referred to as energy weighting, deteriorated detectability. For small microcalcifications or tumors on uniform backgrounds, however, energy subtraction was suboptimal whereas energy weighting provided a minute improvement. The performance was largely independent of beam quality, detector energy resolution, and bin count fraction. It is clear that inclusion of anatomical noise and imaging task in spectral optimization may yield completely different results than an analysis based solely on quantum noise.
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